Large companies that provide goods and/or services to their customers typically use computing systems to produce and store a vast amount of sales and purchase transaction data. The sales and purchase transaction data may be subject to internal company standards as well as accounting best practices and standards. In addition, the data may be subject to the review of governmental entities. In Brazil, for example, billing data related to sales and purchase transactions between companies and government entities must be submitted to the government for approval via the internet. Policies in Brazil necessitate government approval for all sales and purchase transactions, regardless of whether the transaction is with a government entity. These submission and approval requirements are being instituted by governments in many countries around the world.
Government entities or third parties who represent government entities receive sales and purchase transaction data in order to verify that companies pay appropriate taxes on transactions. When companies err by not paying the appropriate taxes on sales and purchase transactions, additional corrective transactions that compensate for errors may be created. Compensating for the errors introduces administrative costs and leads to a larger data footprint. In addition, governments may issue penalties for unpaid or incorrectly paid taxes. One such penalty might be that a company is no longer allowed to buy goods or services. Therefore, creating billing data that complies with governmental requirements can save a company time and money and avoid legal penalties.
Ensuring that data complies with laws or governmental standards may be challenging, given the potential complexity of the laws and the need to apply the appropriate laws to the data. This is true especially in countries like Brazil, which has arguably the most complicated tax regulations in the world. Machine learning can be implemented in instances when developing static rules would be too cumbersome and applying the rules to the data could result in either over- or under-inclusiveness. Using a data-driven approach, machine learning can aptly be applied to situations that involve a large amount of data that is subject to many complicated rules, e.g., vast amounts of sales and purchase transaction data that must comply with governmental standards. Algorithms that use machine learning, as opposed to static rules, may provide for more flexibility as standards change or are added.